Therapeutic Radiology

Response to letter to editor "Radiomics-based machine-learning approach to predict response at brachytherapy using pretreatment magnetic resonance imaging in locally advanced cervical cancer".

Machine learning prediction of local control after Gamma Knife radiosurgery to post-resection cavities from brain metastases: a proof-of-concept study.

BACKGROUND: Large symptomatic brain metastases require initial surgical resection. However, local control (LC) after Gamma Knife radiosurgery (GKRS) to resection cavities remains variable. Quantitative risk stratification using routinely available treatment-time variables could inform surveillance and multidisciplinary decision-making. METHODS: We performed a retrospective study of post-resection ...

Therapeutic Hybrid Intelligence with Neural and Knowledge-based Expert Reasoning for SRS (THINKERS): an AI model for GBM.

BACKGROUND: Prescription dose selection for recurrent glioblastoma treated with Gamma Knife radiosurgery remains highly individualized and is largely ...

A multimodal MRI-radiomics deep learning model for survival risk stratification after gamma knife radiosurgery in patients with brain metastases: A multicenter retrospective study.

BACKGROUND: Brain metastasis (BM) is a high-mortality complication occurring in 20-40% of cancer patients. While the Gamma Knife (GK) is a primary tre...

Intraoperative technological advances and new frontiers in precision glioma surgery.

BACKGROUND: Diffuse gliomas remain among the most surgically challenging tumors, characterized by their infiltrative nature, proximity to eloquent bra...

Intensity-modulated radiotherapy alone versus concurrent chemoradiotherapy ± induction chemotherapy in older patients with low-risk stage II or T3N0M0 nasopharyngeal carcinoma.

BACKGROUND: The optimal treatment strategy for older patients with low-risk stage II or T3N0M0 nasopharyngeal carcinoma (NPC) remains undefined. This ...

Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators.

Objective.This study aimed to develop a dose prediction model for hybrid applicators in cervical cancer brachytherapy.Approach.A total of 216 previous...

Blinded, bias-controlled multi-rater evaluation of human-versus-AI brain metastasis segmentation using a hybrid foundation-model framework.

BACKGROUND: Accurate segmentation of brain metastases (BM) is essential for diagnosis, stereotactic radiosurgery planning, and longitudinal assessment...

Explainable machine learning for patient-specific quality assurance in intensity-modulated radiotherapy based on anatomical structures.

BACKGROUND: Patient-specific quality assurance (PSQA) plays a pivotal role in intensity-modulated radiotherapy (IMRT) to ensure accurate dose delivery...

Deep learning-based dose prediction for MR-guided prostate SIB: Supporting rapid feasibility assessment and adaptive editing margin selection.

BACKGROUND: The template-based treatment planning workflow in the Unity magnetic resonance-guided linear accelerator (MR-Linac) helps facilitate the t...

Gamma Knife treatment planning using knowledge-based reinforcement learning.

BACKGROUND: Inverse planning is often used for Gamma Knife radiosurgery, allowing clinicians to mathematically specify desired clinical objectives and...

Therapeutic Hybrid Intelligence with Neural and Knowledge-based Expert Reasoning for SRS (THINKERS): a mixture-of-experts AI model for vestibular schwannoma.

BACKGROUND: Outcome prediction after Gamma Knife radiosurgery (GKRS) for vestibular schwannoma remains largely guided by tumor size, Koos grade, basel...

[Reirradiation: A new therapeutic paradigm in oncology].

Reirradiation (reRT) has become an essential therapeutic option for selected patients with locoregional recurrences, when surgery or systemic therapie...

Deep learning-based automatic segmentation of rectal tumors in endoscopic images.

PURPOSE: Endoscopy is critical in the identification of rectal tumors, but is prone to observer errors. The aim of this study was to assess the inter-...

Predicting time to local failure after gamma knife radiosurgery for melanoma brain metastases using survival machine learning.

BACKGROUND: Melanoma brain metastases treated with Gamma Knife radiosurgery show heterogeneous local control trajectories. Accurate prediction of time...

Multimodal deep learning integration of clinical, dosiomics, and radiomics features to predict toxicity in breast cancer patients undergoing hypofractionated radiotherapy.

BACKGROUND AND PURPOSE: Radiation dermatitis (RD) and superficial soft tissue fibrosis are common toxicities among the patients with breast cancer rec...

Feasibility of nonspinal bony landmark-based image registration in CyberKnife ® robotic radiotherapy for pelvic and femoral bone tumors.

In stereotactic body radiotherapy for bone metastases using the CyberKnife system, spinal anatomy is generally employed as the alignment reference. Ho...

Local control and dose selection for lung cancer brain metastases treated with radiosurgery: an artificial intelligence model.

Clinical and radiological outcomes after Stereotactic radiosurgery (SRS) for lung cancer brain metastases are heterogeneous, and prescription dose sel...

Machine learning for survival prediction after brain tumor stereotactic radiosurgery: A systematic review and meta-analysis.

Explicit modeling of beam geometry improves three-dimensional dose prediction for esophageal cancer radiotherapy under heterogeneous beam configurations.

PURPOSE: Deep learning-based 3D dose prediction boosts radiotherapy planning efficiency and consistency, yet most models rely solely on anatomical dat...